MIPEX as Measure of Citizenship Models

MIPEX are currently launching their latest release (with a shiny new website), and their data are often used in academic research. Earlier I have shown that the MIPEX can indeed be used as scales — as it is often done –, although there is scope for improving these scales. Put differently, from a statistical point of view, the dimensions and sub-dimensions in the MIPEX data are not optimal. There are two approaches to this: First, we can reduce the data complexity by removing items that are not strongly associated. Second, we can use the redundancy in the data, and pick and mix the data.

In a paper just published in the SSQ, I demonstrate this by recombining bits and pieces of the MIPEX to create citizenship scores that closely match those in Koopmans et al. On the one hand, this is a demonstration that we can easily create more valid constructs when recombining existing data sources like the MIPEX. On the other hand, I have gained classifications of citizenship models in many more countries than previous endeavours — with less effort. As a side product, I can validate the citizenship typology presented by Koopmans et al. by showing the existence of ethnic-pluralistic citizenship models (segregationism), previously only predicted on a theoretical basis.

Koopmans, Ruud, Paul Statham, Marco Giugni, and Florence Passy. 2005. Contested Citizenship: Immigration and Cultural Diversity in Europe. Minneapolis: Minnesota University Press.
Ruedin, Didier. 2015. “Increasing Validity by Recombining Existing Indices: MIPEX as a Measure of Citizenship Models.” Social Science Quarterly. doi:10.1111/ssqu.12162.

Read this before running fsQCA

Just last week I wrote about two papers that examined the validity of QCA. They were by no means the first ones to do so, but that doesn’t make these papers any less important.

Now, QCA isn’t exactly static, even though it remains focused on its founding father. Fuzzyset QCA (fsQCA) is often used these days as it promises to overcome some of the shortcomings of QCA. Unfortunately, even if you buy into the concept and epistemology, the empirics still don’t add up.

Krogslund, Chris, Donghyun Danny Choi, and Mathias Poertner. 2014. “Fuzzy Sets on Shaky Ground: Parameter Sensitivity and Confirmation Bias in fsQCA.” Political Analysis, November, mpu016. doi:10.1093/pan/mpu016.

Krogslund and colleagues used simulations to check how robust fsQCA is. The approach is quite intriguing. Rather than using data generated in the computer as is often done in such situations, they have used three existing studies. After replicating these studies, they modified tiny bits. With a robust method, such tiny changes will not have a substantive impact on the results. With fsQCA, however, the results often changed radically: it is a very sensitive method.